[Mboost-commits] r739 - in pkg/mboostDevel: . inst man tests vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Sep 24 14:50:55 CEST 2013
Author: thothorn
Date: 2013-09-24 14:50:54 +0200 (Tue, 24 Sep 2013)
New Revision: 739
Removed:
pkg/mboostDevel/data/
pkg/mboostDevel/man/Westbc.Rd
pkg/mboostDevel/man/birds.Rd
pkg/mboostDevel/man/bodyfat.Rd
pkg/mboostDevel/man/wpbc.Rd
Modified:
pkg/mboostDevel/inst/birds_Biometrics.R
pkg/mboostDevel/tests/bugfixes.R
pkg/mboostDevel/tests/regtest-baselearner.R
pkg/mboostDevel/tests/regtest-family.R
pkg/mboostDevel/tests/regtest-gamboost.R
pkg/mboostDevel/vignettes/mboost_illustrations.Rnw
pkg/mboostDevel/vignettes/mboost_tutorial.Rnw
Log:
mv data to TH.data
Modified: pkg/mboostDevel/inst/birds_Biometrics.R
===================================================================
--- pkg/mboostDevel/inst/birds_Biometrics.R 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/inst/birds_Biometrics.R 2013-09-24 12:50:54 UTC (rev 739)
@@ -1,6 +1,6 @@
library("mboostDevel")
-data("birds", package = "mboostDevel")
+data("birds", package = "TH.data")
# define characteristics of the boosting algorithm
bcr <- boost_control(mstop=200, trace=TRUE)
Deleted: pkg/mboostDevel/man/Westbc.Rd
===================================================================
--- pkg/mboostDevel/man/Westbc.Rd 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/man/Westbc.Rd 2013-09-24 12:50:54 UTC (rev 739)
@@ -1,49 +0,0 @@
-\name{Westbc}
-\alias{Westbc}
-\docType{data}
-\title{ Breast Cancer Gene Expression }
-\description{
- Gene expressions for 7129 genes in 49 breast cancer samples and the
- status of lymph node involvement.
-}
-\usage{data("Westbc")}
-\format{
- An list with two elements to be converted to class \code{ExpressionSet} (see package \code{Biobase}).
-}
-\details{
-
- A full description of the data
- can be found in West et al. (2001) and an application
- of boosted linear models is given by Buehlmann (2006).
-
-}
-\references{
-
- Peter Buehlmann (2006), Boosting for high-dimensional linear models.
- \emph{The Annals of Statistics}, \bold{34}(2), 559--583.
-
- Peter Buehlmann and Torsten Hothorn (2007),
- Boosting algorithms: regularization, prediction and model fitting.
- \emph{Statistical Science}, \bold{22}(4), 477--505.
-
-}
-\source{
- Mike West, Carrie Blanchette, Holly Dressman, Erich Huang,
- Seiichi Ishida, Rainer Spang, Harry Zuzan, John A. Olson Jr.,
- Jeffrey R. Marks and Joseph R. Nevins (2001),
- Predicting the clinical status of human breast cancer by using
- gene expression profiles, \emph{Proceedings of the National Academy of Sciences},
- \bold{98}, 11462-11467.
- \url{http://data.cgt.duke.edu/west.php}
-}
-\examples{
-
- \dontrun{
- library("Biobase")
- data("Westbc", package = "mboostDevel")
- westbc <- new("ExpressionSet",
- phenoData = new("AnnotatedDataFrame", data = Westbc$pheno),
- assayData = assayDataNew(exprs = Westbc$assay))
- }
-}
-\keyword{datasets}
Deleted: pkg/mboostDevel/man/birds.Rd
===================================================================
--- pkg/mboostDevel/man/birds.Rd 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/man/birds.Rd 2013-09-24 12:50:54 UTC (rev 739)
@@ -1,55 +0,0 @@
-\name{birds}
-\alias{birds}
-\docType{data}
-\title{ Habitat Suitability for Breeding Bird Communities }
-\description{
- Environmental variables and bird counts for identifying
- suitable bird habitats
-}
-\usage{data("birds")}
-\format{
- A data frame with 258 observations on the following 10 variables.
- \describe{
- \item{\code{GST}}{Growing stock per grid}
- \item{\code{DBH}}{Mean diameter of the largest three trees}
- \item{\code{AOT}}{Age of oldest tree}
- \item{\code{AFS}}{Age of forest stand}
- \item{\code{DWC}}{Amount of dead wood of conifers}
- \item{\code{LOG}}{Amount of logs per grid}
- \item{\code{x_gk}}{grid location, x coordinate}
- \item{\code{y_gk}}{grid location, y coordinate}
- \item{\code{SG4}}{observed number of birds from structural gild 4:
- Requirement of regeneration (Phylloscopus trochilus, Aegithalos caudatus)}
- \item{\code{SG5}}{observed number of birds from structural gild 5:
- Requirement of regeneration combined with planted conifers (Phylloscopus collybita, Turdus
- merula, Sylvia atricapilla).}
- }
-}
-\details{
-
-Counts of breeding bird
-communities collected at 258 observation plots in a northern
-Bavarian forest district are the response variable of interest.
-Along with the number of birds in two structural gilds,
-6 covariates are given here and
-one is interested in quantifying their impact on habitat
-suitability.
-
-}
-\source{
-
- Joerg Mueller (2005).
- Forest structures as key factor for beetle and bird communities
- in beech forests. PhD thesis, Munich University of Technology.
- \url{http://mediatum.ub.tum.de}
-
-}
-\references{
-
- Thomas Kneib and Joerg Mueller and Torsten Hothorn (2008),
- Spatial smoothing techniques for the assessment of habitat suitability,
- \emph{Environmental and Ecological Statistics},
- \bold{15}(3), 343--364.
-
-}
-\keyword{datasets}
Deleted: pkg/mboostDevel/man/bodyfat.Rd
===================================================================
--- pkg/mboostDevel/man/bodyfat.Rd 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/man/bodyfat.Rd 2013-09-24 12:50:54 UTC (rev 739)
@@ -1,79 +0,0 @@
-\name{bodyfat}
-\alias{bodyfat}
-\docType{data}
-\title{ Prediction of Body Fat by Skinfold Thickness, Circumferences, and
- Bone Breadths}
-\description{
- For 71 healthy female subjects, body fat measurements and several
- anthropometric measurements are available for predictive modelling
- of body fat.
-}
-\usage{data("bodyfat")}
-\format{
- A data frame with 71 observations on the following 10 variables.
- \describe{
- \item{\code{age}}{age in years.}
- \item{\code{DEXfat}}{body fat measured by DXA, response variable.}
- \item{\code{waistcirc}}{waist circumference.}
- \item{\code{hipcirc}}{hip circumference.}
- \item{\code{elbowbreadth}}{breadth of the elbow.}
- \item{\code{kneebreadth}}{breadth of the knee.}
- \item{\code{anthro3a}}{sum of logarithm of three anthropometric measurements.}
- \item{\code{anthro3b}}{sum of logarithm of three anthropometric measurements.}
- \item{\code{anthro3c}}{sum of logarithm of three anthropometric measurements.}
- \item{\code{anthro4}}{sum of logarithm of three anthropometric measurements.}
- }
-}
-\details{
-
- Garcia et al. (2005) report on the development of predictive regression equations
- for body fat content by means of common anthropometric
- measurements which were obtained for 71 healthy German women.
- In addition, the women's body composition was measured by
- Dual Energy X-Ray Absorptiometry (DXA). This reference method
- is very accurate in measuring body fat but finds little applicability
- in practical environments, mainly because of high costs and the
- methodological efforts needed. Therefore, a simple regression equation
- for predicting DXA measurements of body fat is of special interest for the practitioner.
- Backward-elimination was applied to select
- important variables from the available anthropometrical measurements, and
- Garcia (2005) report a final linear model utilizing
- hip circumference, knee breadth and a compound covariate which is defined as
- the sum of log chin skinfold, log triceps skinfold and log subscapular skinfold.
-
-}
-\source{
-
- Ada L. Garcia, Karen Wagner, Torsten Hothorn, Corinna Koebnick,
- Hans-Joachim F. Zunft and Ulrike Trippo (2005),
- Improved prediction of body fat by measuring skinfold
- thickness, circumferences, and bone breadths. \emph{Obesity Research},
- \bold{13}(3), 626--634.
-
- Peter Buehlmann and Torsten Hothorn (2007),
- Boosting algorithms: regularization, prediction and model fitting.
- \emph{Statistical Science}, \bold{22}(4), 477--505.
-
- Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
- (2012). Model-based Boosting in R: A Hands-on Tutorial Using the R
- Package mboost. \emph{Computational Statistics}.\cr
- \url{http://dx.doi.org/10.1007/s00180-012-0382-5}
-
- Available as vignette via: vignette(package = "mboostDevel", "mboost_tutorial")
-}
-\examples{
-
- data("bodyfat", package = "mboostDevel")
-
- ### final model proposed by Garcia et al. (2005)
- fmod <- lm(DEXfat ~ hipcirc + anthro3a + kneebreadth, data = bodyfat)
- coef(fmod)
-
- ### plot additive model for same variables
- amod <- gamboost(DEXfat ~ hipcirc + anthro3a + kneebreadth,
- data = bodyfat, baselearner = "bbs")
- layout(matrix(1:3, ncol = 3))
- plot(amod[mstop(AIC(amod, "corrected"))], ask = FALSE)
-
-}
-\keyword{datasets}
Deleted: pkg/mboostDevel/man/wpbc.Rd
===================================================================
--- pkg/mboostDevel/man/wpbc.Rd 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/man/wpbc.Rd 2013-09-24 12:50:54 UTC (rev 739)
@@ -1,102 +0,0 @@
-\name{wpbc}
-\alias{wpbc}
-\docType{data}
-\title{ Wisconsin Prognostic Breast Cancer Data}
-\description{
-
- Each record represents follow-up data for one breast cancer
- case. These are consecutive patients seen by Dr. Wolberg
- since 1984, and include only those cases exhibiting invasive
- breast cancer and no evidence of distant metastases at the
- time of diagnosis.
-
-}
-\usage{data("wpbc")}
-\format{
- A data frame with 198 observations on the following 34 variables.
- \describe{
- \item{\code{status}}{a factor with levels \code{N} (nonrecur) and
- \code{R} (recur)}
- \item{\code{time}}{recurrence time (for \code{status == "R"}) or
- disease-free time (for \code{status == "N"}). }
- \item{\code{mean_radius}}{radius (mean of distances from center to points on the perimeter) (mean).}
- \item{\code{mean_texture}}{texture (standard deviation of gray-scale values) (mean).}
- \item{\code{mean_perimeter}}{perimeter (mean).}
- \item{\code{mean_area}}{area (mean).}
- \item{\code{mean_smoothness}}{smoothness (local variation in radius lengths) (mean).}
- \item{\code{mean_compactness}}{compactness (mean).}
- \item{\code{mean_concavity}}{concavity (severity of concave portions of the contour) (mean).}
- \item{\code{mean_concavepoints}}{concave points (number of concave portions of the contour) (mean).}
- \item{\code{mean_symmetry}}{symmetry (mean).}
- \item{\code{mean_fractaldim}}{fractal dimension (mean).}
- \item{\code{SE_radius}}{radius (mean of distances from center to points on the perimeter) (SE).}
- \item{\code{SE_texture}}{texture (standard deviation of gray-scale values) (SE).}
- \item{\code{SE_perimeter}}{perimeter (SE).}
- \item{\code{SE_area}}{area (SE).}
- \item{\code{SE_smoothness}}{smoothness (local variation in radius lengths) (SE).}
- \item{\code{SE_compactness}}{compactness (SE).}
- \item{\code{SE_concavity}}{concavity (severity of concave portions of the contour) (SE).}
- \item{\code{SE_concavepoints}}{concave points (number of concave portions of the contour) (SE).}
- \item{\code{SE_symmetry}}{symmetry (SE).}
- \item{\code{SE_fractaldim}}{fractal dimension (SE).}
- \item{\code{worst_radius}}{radius (mean of distances from center to points on the perimeter) (worst).}
- \item{\code{worst_texture}}{texture (standard deviation of gray-scale values) (worst).}
- \item{\code{worst_perimeter}}{perimeter (worst).}
- \item{\code{worst_area}}{area (worst).}
- \item{\code{worst_smoothness}}{smoothness (local variation in radius lengths) (worst).}
- \item{\code{worst_compactness}}{compactness (worst).}
- \item{\code{worst_concavity}}{concavity (severity of concave portions of the contour) (worst).}
- \item{\code{worst_concavepoints}}{concave points (number of concave portions of the contour) (worst).}
- \item{\code{worst_symmetry}}{symmetry (worst).}
- \item{\code{worst_fractaldim}}{fractal dimension (worst).}
- \item{\code{tsize}}{diameter of the excised tumor in centimeters.}
- \item{\code{pnodes}}{number of positive axillary lymph nodes observed at time of surgery.}
- }
-}
-\details{
-
- The first 30 features are computed from a digitized image of a
- fine needle aspirate (FNA) of a breast mass. They describe
- characteristics of the cell nuclei present in the image.
-
- There are two possible learning problems: predicting \code{status} or predicting
- the time to recur.
-
- 1) Predicting field 2, outcome: R = recurrent, N = non-recurrent
- - Dataset should first be filtered to reflect a particular
- endpoint; e.g., recurrences before 24 months = positive,
- non-recurrence beyond 24 months = negative.
- - 86.3% accuracy estimated accuracy on 2-year recurrence using
- previous version of this data.
-
- 2) Predicting Time To Recur (field 3 in recurrent records)
- - Estimated mean error 13.9 months using Recurrence Surface
- Approximation.
-
- The data are originally available from the UCI machine learning repository, see
- \url{http://www.ics.uci.edu/~mlearn/databases/breast-cancer-wisconsin/}.
-
-}
-\source{
-
- W. Nick Street, Olvi L. Mangasarian and William H. Wolberg (1995).
- An inductive learning approach to prognostic prediction.
- In A. Prieditis and S. Russell, editors, \emph{Proceedings of the
- Twelfth International Conference on Machine Learning}, pages
- 522--530, San Francisco, Morgan Kaufmann.
-
- Peter Buehlmann and Torsten Hothorn (2007),
- Boosting algorithms: regularization, prediction and model fitting.
- \emph{Statistical Science}, \bold{22}(4), 477--505.
-
-}
-\examples{
-
- data("wpbc", package = "mboostDevel")
-
- ### fit logistic regression model with 100 boosting iterations
- coef(glmboost(status ~ ., data = wpbc[,colnames(wpbc) != "time"],
- family = Binomial()))
-
-}
-\keyword{datasets}
Modified: pkg/mboostDevel/tests/bugfixes.R
===================================================================
--- pkg/mboostDevel/tests/bugfixes.R 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/tests/bugfixes.R 2013-09-24 12:50:54 UTC (rev 739)
@@ -31,7 +31,7 @@
### blackboost _did_ touch the response, arg!
-data("bodyfat", package = "mboostDevel")
+data("bodyfat", package = "TH.data")
ctrl <- boost_control(mstop = 500, nu = 0.01)
bb <- blackboost(DEXfat ~ ., data = bodyfat, control = ctrl)
n <- nrow(bodyfat)
@@ -71,7 +71,7 @@
if (FALSE){
### dfbase=1 was not working correctly for ssp
### spotted by Matthias Schmid <Matthias.Schmid at imbe.imed.uni-erlangen.de>
-data("bodyfat", package = "mboostDevel")
+data("bodyfat", package = "TH.data")
ctrl <- boost_control(mstop = 100)
### COMMENT: Not using ssp here but P-splines
### Remove check!
@@ -148,7 +148,7 @@
### check gamboost with weights (use weighted some of residuals
### for variable selection)
-data("bodyfat", package = "mboostDevel")
+data("bodyfat", package = "TH.data")
set.seed(290875)
n <- nrow(bodyfat)
Modified: pkg/mboostDevel/tests/regtest-baselearner.R
===================================================================
--- pkg/mboostDevel/tests/regtest-baselearner.R 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/tests/regtest-baselearner.R 2013-09-24 12:50:54 UTC (rev 739)
@@ -139,7 +139,7 @@
stopifnot(abs(truedf - 4) < sqrt(.Machine$double.eps))
### check accuracy of df2lambda
-data("bodyfat", package="mboostDevel")
+data("bodyfat", package="TH.data")
diff_df <- matrix(NA, nrow=8, ncol=ncol(bodyfat))
rownames(diff_df) <- paste("df", 3:10)
colnames(diff_df) <- names(bodyfat)
Modified: pkg/mboostDevel/tests/regtest-family.R
===================================================================
--- pkg/mboostDevel/tests/regtest-family.R 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/tests/regtest-family.R 2013-09-24 12:50:54 UTC (rev 739)
@@ -137,7 +137,7 @@
### AUC
-data("wpbc", package = "mboostDevel")
+data("wpbc", package = "TH.data")
wpbc[,colnames(wpbc) != "status"] <- scale(wpbc[,colnames(wpbc) != "status"])
wpbc <- wpbc[complete.cases(wpbc), colnames(wpbc) != "time"]
mAUC <- gamboost(status ~ ., data = wpbc, family = AUC())
Modified: pkg/mboostDevel/tests/regtest-gamboost.R
===================================================================
--- pkg/mboostDevel/tests/regtest-gamboost.R 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/tests/regtest-gamboost.R 2013-09-24 12:50:54 UTC (rev 739)
@@ -35,7 +35,7 @@
### check boosting hat matrix with multiple independent variables
### and weights
-data("bodyfat", package = "mboostDevel")
+data("bodyfat", package = "TH.data")
bffm <- DEXfat ~ age + waistcirc + hipcirc + elbowbreadth + kneebreadth +
anthro3a + anthro3b + anthro3c + anthro4
indep <- names(bodyfat)[names(bodyfat) != "DEXfat"]
@@ -163,7 +163,7 @@
stopifnot(tmp < 1e-5)
### predictions:
-data("bodyfat", package = "mboostDevel")
+data("bodyfat", package = "TH.data")
amod <- gamboost(DEXfat ~ hipcirc + anthro3a, data = bodyfat, baselearner = "bbs")
agg <- c("none", "sum", "cumsum")
@@ -249,7 +249,7 @@
stopifnot(pr - pr2 < sqrt(.Machine$double.eps))
### coefficients:
-data("bodyfat", package = "mboostDevel")
+data("bodyfat", package = "TH.data")
amod <- gamboost(DEXfat ~ hipcirc + anthro3a + kneebreadth,
data = bodyfat, baselearner = "bbs")
stopifnot(length(coef(amod)) == 3)
Modified: pkg/mboostDevel/vignettes/mboost_illustrations.Rnw
===================================================================
--- pkg/mboostDevel/vignettes/mboost_illustrations.Rnw 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/vignettes/mboost_illustrations.Rnw 2013-09-24 12:50:54 UTC (rev 739)
@@ -11,7 +11,7 @@
%%\usepackage{Sweave}
%%\VignetteIndexEntry{mboost Illustrations}
-%%\VignetteDepends{mboostDevel, survival}
+%%\VignetteDepends{mboostDevel, survival, TH.data}
\newcommand{\Rpackage}[1]{{\normalfont\fontseries{b}\selectfont #1}}
\newcommand{\Robject}[1]{\texttt{#1}}
@@ -350,7 +350,7 @@
source("setup.R")
### OK, this once required Biobase and is a very dirty hack...
-data("Westbc", package = "mboostDevel")
+data("Westbc", package = "TH.data")
westbc <- Westbc
exprs <- function(x) x$assay
pData <- function(x) x$pheno
Modified: pkg/mboostDevel/vignettes/mboost_tutorial.Rnw
===================================================================
--- pkg/mboostDevel/vignettes/mboost_tutorial.Rnw 2013-09-12 14:57:20 UTC (rev 738)
+++ pkg/mboostDevel/vignettes/mboost_tutorial.Rnw 2013-09-24 12:50:54 UTC (rev 739)
@@ -3,7 +3,7 @@
\documentclass[10pt]{scrartcl}
%%\VignetteIndexEntry{mboost Tutorial}
-%%\VignetteDepends{mboostDevel,lattice,RColorBrewer}
+%%\VignetteDepends{mboostDevel,lattice,RColorBrewer,TH.data}
\usepackage{threeparttable} % needed for \tnote (footnotes in table)
\usepackage[utf8]{inputenc}
@@ -496,7 +496,7 @@
<<setup, echo = true, results = hide>>=
library("mboostDevel") ## load package
-data("bodyfat") ## load data
+data("bodyfat", package = "TH.data") ## load data
@
The response variable is the body fat measured by DXA (\R{DEXfat}), which can be
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